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|Title:||A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction||Authors:||Sales, Márcio H.
de Bruin, Sytze
Souza, Carlos Moreira
|Keywords:||Land cover models;Deforestation;Spatiotemporal modeling;Hurdle models||Category:||Civil Engineering;Civil Engineering||Field:||Engineering and Technology||Issue Date:||Aug-2017||Publisher:||Science Direct||Source:||Spatial Statistics, Volume 21, Part A, 2017, Pages 304-318||metadata.dc.doi:||https://doi.org/10.1016/j.spasta.2017.06.003||Abstract:||This paper introduces and tests a geostatistical spatiotemporal hurdle approach for predicting the spatial distribution of future deforestation (one to three years ahead in time). The method accounts for neighborhood effects by modeling the auto-correlation of occurrence and intensity of deforestation, using a spatiotemporal geostatistical specification. Deforestation observations are modeled as a function of pertinent control variables, such as distance to roads and protected areas, and the model accounts for space–time autocorrelated residuals with non-stationary variance. Applied to the Brazilian Amazon, the model predicted the locations of new deforestation events with over 90% agreement. In addition, 100% of the deforestation intensity values were contained in the model’s confidence bounds. The features of the model and validation results qualify the model as a strong candidate for short-term deforestation modeling.||URI:||http://ktisis.cut.ac.cy/handle/10488/10291||ISSN:||22116753||Rights:||© 2017 Elsevier B.V. All rights reserved.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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